While was designed with flexibility and extensibility in mind, there is also a less flexible but significantly faster Cython/C engine for reading and writing ASCII files. By default, read() and write() will attempt to use this engine when dealing with compatible formats. The following formats are currently compatible with the fast engine:

  • basic

  • commented_header

  • csv

  • no_header

  • rdb

  • tab

The fast engine can also be enabled through the format parameter by prefixing a compatible format with “fast” and then an underscore. In this case, or when enforcing the fast engine by either setting fast_reader='force' or explicitly setting any of the Fast Conversion Options, read() will not fall back on an ordinary reader if fast reading fails.


To open a CSV file and write it back out:

>>> from astropy.table import Table
>>> t ='file.csv', format='fast_csv')  
>>> t.write('output.csv', format='ascii.fast_csv')  

To disable the fast engine, specify fast_reader=False or fast_writer=False. For example:

>>> t ='file.csv', format='csv', fast_reader=False) 
>>> t.write('file.csv', format='csv', fast_writer=False) 


Guessing and Fast reading

By default read() will try to guess the format of the input data by successively trying different formats until one succeeds (see the section on Guess Table Format). For each supported format it will first try the fast, then the slow version of that reader. Without any additional options this means that both some pure Python readers with no fast implementation and the Python versions of some readers will be tried before getting to some of the fast readers. To bypass them entirely, a fast reader should be explicitly requested as above.

For optimum performance however, it is recommended to turn off guessing entirely (guess=False) or narrow down the format options as much as possible by specifying the format (e.g., format='csv') and/or other options such as the delimiter.


Since the fast engine is not part of the ordinary infrastructure, fast readers raise an error when passed certain parameters which are not implemented in the fast reader infrastructure. In this case read() will fall back on the ordinary reader, unless the fast reader has been explicitly requested (see above). These parameters are:

  • Negative header_start (except for commented-header format)

  • Negative data_start

  • data_start=None

  • comment string not of length 1

  • delimiter string not of length 1

  • quotechar string not of length 1

  • converters

  • outputter_cls

  • inputter_cls

  • data_splitter_cls

  • header_splitter_cls

Fast Conversion Options#

In addition to True and False, the parameter fast_reader can also be a dict specifying any of two additional parameters, use_fast_converter and exponent_style.


To specify additional parameters using fast_reader:

>>>'data.txt', format='basic',
...            fast_reader={'use_fast_converter': True}) 

These options allow for even faster table reading when enabled, but both are disabled by default because they come with some caveats.

Setting use_fast_converter to be True enables a faster but slightly imprecise conversion method for floating-point values, as described below.

The exponent_style parameter allows to define a different character from the default 'e' for exponential formats in the input file. The special setting 'fortran' enables auto-detection of any valid exponent character under Fortran notation. For details see the section on Fortran-Style Exponents.

Fast Converter#

Input floating-point values should ideally be converted to the nearest possible floating-point approximation; that is, the conversion should be correct within half of the distance between the two closest representable values, or 0.5 ULP. The ordinary readers, as well as the default fast reader, are guaranteed to convert floating-point values within 0.5 ULP, but there is also a faster and less accurate conversion method accessible via use_fast_converter. If the input data has less than about fifteen significant figures, or if accuracy is relatively unimportant, this converter might be the best option in performance-critical scenarios.

For values with a reasonably small number of significant figures, the fast converter is guaranteed to produce an optimal conversion (within 0.5 ULP). Once the number of significant figures exceeds the precision of 64-bit floating-point values, the fast converter is no longer guaranteed to be within 0.5 ULP, but about 60% of values end up within 0.5 ULP and about 90% within 1.0 ULP.

Reading Large Tables#

For reading very large tables using the fast reader, see the section on Reading Large Tables in Chunks.


The fast engine supports the same functionality as the ordinary writing engine and is generally about two to four times faster than the ordinary engine. The speed advantage of the faster engine is greatest for integer data and least for floating-point data; the fast engine is around 3.6 times faster for a sample file including a mixture of floating-point, integer, and text data. Also note that stripping string values slows down the writing process, so specifying strip_whitespace=False can improve performance.

Speed Gains#

The fast ASCII engine was designed based on the general parsing strategy used in the Pandas data analysis library, so its performance is generally comparable (although slightly slower by default) to the Pandas read_csv method.

The genfromtxt and the ordinary reader are very similar in terms of speed, while read_csv is slightly faster than the fast engine for integer and floating-point data; for pure floating-point data, enabling the fast converter yields a speedup of about 50%. Also note that Pandas uses the exact same method as the fast converter in Astropy when converting floating-point data.

The difference in performance between the fast engine and Pandas for text data depends on the extent to which data values are repeated, as Pandas is almost twice as fast as the fast engine when every value is identical and the reverse is true when values are randomized. This is because the fast engine uses fixed-size NumPy string arrays for text data, while Pandas uses variable-size object arrays and uses an underlying set to avoid copying repeated values.

Overall, the fast engine tends to be around four or five times faster than the ordinary ASCII engine. If the input data is very large (generally about 100,000 rows or greater), and particularly if the data does not contain primarily integer data or repeated string values.

Another point worth noting is that the fast engine uses memory mapping if a filename is supplied as input. If you want to avoid this for whatever reason, supply an open file object instead. However, this will generally be less efficient from both a time and a memory perspective, as the entire file input will have to be read at once.